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Probabilistic Graphical Models Principles and Techniques

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ISBN-10: 0262013193

ISBN-13: 9780262013192

Edition: 2009

Authors: Daphne Koller, Nir Friedman, D. Koller, Francis Bach

List price: $125.00
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Book details

List price: $125.00
Copyright year: 2009
Publisher: MIT Press
Publication date: 7/31/2009
Binding: Hardcover
Pages: 1270
Size: 8.31" wide x 9.37" long x 1.97" tall
Weight: 5.126
Language: English

Nir Friedman is Professor in the Department of Computer Science and Engineering at Hebrew University.

Complete Table of Contents
Acknowledgments
List of Figures
List of Algorithms
List of Boxes
Introduction
Motivation
Structured Probabilistic Models
Overview and Roadmap
Historical Notes
Foundations
Probability Theory
Graphs
Relevant Literature
Exercises
Representation
The Bayesian Network Representation
Exploiting Independence Properties
Bayesian Networks
Independencies in Graphs
From Distributions to Graphs
Summary
Relevant Literature
Exercises
Undirected Graphical Models
The Misconception Example
Parameterization
Markov Network Independencies
Parameterization Revisited
Bayesian Networks and Markov Networks
Partially Directed Models
Summary and Discussion
Relevant Literature
Exercises
Local Probabilistic Models
Tabular CPDs
Deterministic CPDs
Context-Specific CPDs
Independence of Causal Influence
Continuous Variables
Conditional Bayesian Networks
Summary
Relevant Literature
Exercises
Template-Based Representations
Introduction
Temporal Models
Template Variables and Template Factors
Directed Probabilistic Models for Object-Relational Domains
Undirected Representation
Structural Uncertainty
Summary
Relevant Literature
Exercises
Gaussian Network Models
Multivariate Gaussians
Gaussian Bayesian Networks
Gaussian Markov Random Fields
Summary
Relevant Literature
Exercises
The Exponential Family
Introduction
Exponential Families
Factored Exponential Families
Entropy and Relative Entropy
Projections
Summary
Relevant Literature
Exercises
Inference
Exact Inference: Variable Elimination
Analysis of Complexity
Variable Elimination: The Basic Ideas
Variable Elimination
Complexity and Graph Structure: Variable Elimination
Conditioning
Inference with Structured CPDs
Summary and Discussion
Relevant Literature
Exercises
Exact Inference: Clique Trees
Variable Elimination and Clique Trees
Message Passing: Sum Product
Message Passing: Belief Update
Constructing a Clique Tree
Summary
Relevant Literature
Exercises
Inference as Optimization
Introduction
Exact Inference as Optimization
Propagation-Based Approximation
Propagation with Approximate Messages
Structured Variational Approximations
Summary and Discussion
Relevant Literature
Exercises
Particle-Based Approximate Inference
Forward Sampling
Likelihood Weighting and Importance Sampling
Markov Chain Monte Carlo Methods
Collapsed Particles
Deterministic Search Methods
Summary
Relevant Literature
Exercises
MAP Inference
Overview
Variable Elimination for (Marginal) MAP
Max-Product in Clique Trees
Max-Product Belief Propagation in Loopy Cluster Graphs
MAP as a Linear Optimization Problem
Using Graph Cuts for MAP
Local Search Algorithms
Summary
Relevant Literature
Exercises
Inference in Hybrid Networks
Introduction
Variable Elimination in Gaussian Networks
Hybrid Networks
Nonlinear Dependencies
Particle-Based Approximation Methods
Summary and Discussion
Relevant Literature
Exercises
Inference in Temporal Models
Inference Tasks
Exact Inference
Approximate Inference
Hybrid DBNs
Summary
Relevant Literature
Exercises
Learning
Learning Graphical Models: Overview
Motivation
Goals of Learning
Learning as Optimization
Learning Tasks
Relevant Literature
Parameter Estimation
Maximum Likelihood Estimation
MLE for Bayesian Networks
Bayesian Parameter Estimation
Bayesian Parameter Estimation in Bayesian Networks
Learning Models with Shared Parameters
Generalization Analysis
Summary
Relevant Literature
Exercises
Structure Learning in Bayesian Networks
Introduction
Constraint-Based Approaches
Structure Scores
Structure Search
Bayesian Model Averaging
Learning Models with Additional Structure
Summary and Discussion
Relevant Literature
Exercises
Partially Observed Data
Foundations
Parameter Estimation
Bayesian Learning with Incomplete Data
Structure Learning
Learning Models with Hidden Variables
Summary
Relevant Literature
Exercises
Learning Undirected Models
Overview
The Likelihood Function
Maximum (Conditional) Likelihood Parameter Estimation
Parameter Priors and Regularization
Learning with Approximate Inference
Alternative Objectives
Structure Learning
Summary
Relevant Literature
Exercises
Actions and Decisions
Causality
Motivation and Overview
Causal Models
Structural Causal Identifiability
Mechanisms and Response Variables
Partial Identifiability in Functional Causal Models
Counterfactual Queries
Learning Causal Models
Summary
Relevant Literature
Exercises
Utilities and Decisions
Foundations: Maximizing Expected Utility
Utility Curves
Utility Elicitation
Utilities of Complex Outcomes
Summary
Relevant Literature
Exercises
Structured Decision Problems
Decision Trees
Influence Diagrams
Backward Induction in Influence Diagrams
Computing Expected Utilities
Optimization in Influence Diagrams
Ignoring Irrelevant Information
Value of Information
Summary
Relevant Literature
Exercises
Epilogue
Background Material
Information Theory
Convergence Bounds
Algorithms and Algorithmic Complexity
Combinatorial Optimization and Search
Continuous Optimization
Bibliography
Notation Index
Subject Index
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